Iterative Cross-correlation Research Articles

Optimization and Quality Assessment of Arrival Time Picking for Downhole Microseismic Events.

Arrival-time picking is a critical step in microseismic data processing, and thus the quality control of arrival results is necessary. Conventional picking methods may be inaccurate or inconsistent due to varied signal-to-noise ratios (SNR) and waveform patterns of the events recorded in different time sections. To address this issue, we propose a quality assessment method based on waveform similarity coefficients to evaluate arrival results and also a global optimization algorithm based on iterative cross-correlation to refine arrival times. The recordings after moveout correction are applied to calculate the intra-event and inter-event waveform coefficients for the quality assessment of arrival results. The residual time differences of intra-event and inter-event traces are calculated sequentially using an enhanced iterative cross-correlation method. In addition, the stacked waveform of each event after the intra-event residual time correction is introduced for global optimization to obtain the inter-event residual time discrepancies. We use both synthetic data and field data to validate the proposed method. The results indicate that the proposed method yields more robust and reliable results. The quality assessment of the optimized arrivals is greatly enhanced compared to the adjusted picks obtained from single event-based processing methods.

Early Results of Time Domain Receiver Function Data Processing in Mt Merapi and Mt Merbabu

The receiver function method is a method to image the earth subsurface by utilizing Ps conversion waves that are formed due to the velocity boundary. In general, the receiver function method estimates depth of structures such as the mantle-crust boundary by deconvoluting the vertical component from the horizontal component. Typical receiver function data processing is done in the frequency domain where the deconvolution process can be seen as a division between two components. In this study, we tried to reprocess the data using a deconvolution technique in time domain, popularly known as iterative time-domain deconvolution. The principle of iterative time domain deconvolution consists of iterative cross-correlation between the horizontal and vertical component. We use data from the DOMERAPI seismic station network located in the vicinity of Mt Merapi and Mt Merbabu. Mt Merapi is one of the most active volcanoes in the world with frequent eruptions and located at the ring of fire chain volcano in Indonesia. Note that the previous receiver function study in this region showed complex signals at some stations that may be related to sediment at shallow sediment and possible layers of low velocity zone that interfering main signal for a crust-mantle boundary. Our current results show iterative time domain RFs have clearer and smoother signal than the frequency domain that help interpreting the waveform signals. We estimate a range of crust thickness between 26-31 km near Mt Merapi. However, we noticed that iterative time domain calculation requires longer computation time and input signal.

Preliminary result for crustal properties derivation related to tectonics for hazard mitigation in Eastern Indonesia using Teleseismic P Coda

Eastern Indonesia is tectonically complex, formed by different plates and microplates interactions from different origins. This complexity gives geoscientists a challenge to solve the ’jigsaw’ of the complex interactions. The understanding of tectonic processes can lead to a breakthrough in both resource exploration and disaster risk reduction. We utilize teleseismic P wave coda for random coda from scattering and deterministic coda originated from the crust-mantle boundary (Moho) to derive the crustal properties, including thickness, Vp/Vs, and qualitative scattering characteristics. For the scattering properties, we apply Iterative Cross-Correlation and Stacking (ICCS) to align the waveform. At the same time, for the crust characteristic, we employ the Receiver Functions (RF) method alongside H-k stacking. The crustal thickness recovered from the RF and H-k stacking has a good correlation with the crustal origin, where the thickness in older and stable crust originated from Sundaland and Gondwana is thicker than a younger plate of the crust arc and subduction origin. The Vp/Vs is high in a region that is interpreted to be dominated by mafic lower crust originated from oceanic-oceanic subduction during Eocene, anisotropy, or by a magmatic anomaly. The P coda also correlated well with the subsurface magmatic anomaly by providing a unique pattern.

Dense velocity reconstruction from tomographic PTV with material derivatives

A method is proposed to reconstruct the instantaneous velocity field from time-resolved volumetric particle tracking velocimetry (PTV, e.g., 3D-PTV, tomographic PTV and Shake-the-Box), employing both the instantaneous velocity and the velocity material derivative of the sparse tracer particles. The constraint to the measured temporal derivative of the PTV particle tracks improves the consistency of the reconstructed velocity field. The method is christened as pouring time into space, as it leverages temporal information to increase the spatial resolution of volumetric PTV measurements. This approach becomes relevant in cases where the spatial resolution is limited by the seeding concentration. The method solves an optimization problem to find the vorticity and velocity fields that minimize a cost function, which includes next to instantaneous velocity, also the velocity material derivative. The velocity and its material derivative are related through the vorticity transport equation, and the cost function is minimized using the limited-memory Broyden–Fletcher–Goldfarb–Shanno (L-BFGS) algorithm. The procedure is assessed numerically with a simulated PTV experiment in a turbulent boundary layer from a direct numerical simulation (DNS). The experimental validation considers a tomographic particle image velocimetry (PIV) experiment in a similar turbulent boundary layer and the additional case of a jet flow. The proposed technique (‘vortex-in-cell plus’, VIC+) is compared to tomographic PIV analysis (3D iterative cross-correlation), PTV interpolation methods (linear and adaptive Gaussian windowing) and to vortex-in-cell (VIC) interpolation without the material derivative. A visible increase in resolved details in the turbulent structures is obtained with the VIC+ approach, both in numerical simulations and experiments. This results in a more accurate determination of the turbulent stresses distribution in turbulent boundary layer investigations. Data from a jet experiment, where the vortex topology is retrieved with a small number of tracers indicate the potential utilization of VIC+ in low-concentration experiments as for instance occurring in large-scale volumetric PTV measurements.

SharpViSu: integrated analysis and segmentation of super-resolution microscopy data.

Summary: We introduce SharpViSu, an interactive open-source software with a graphical user interface, which allows performing processing steps for localization data in an integrated manner. This includes common features and new tools such as correction of chromatic aberrations, drift correction based on iterative cross-correlation calculations, selection of localization events, reconstruction of 2D and 3D datasets in different representations, estimation of resolution by Fourier ring correlation, clustering analysis based on Voronoi diagrams and Ripley’s functions. SharpViSu is optimized to work with eventlist tables exported from most popular localization software. We show applications of these on single and double-labelled super-resolution data.Availability and implementation: SharpViSu is available as open source code and as compiled stand-alone application under https://github.com/andronovl/SharpViSu.Contact: klaholz@igbmc.frSupplementary information: Supplementary data are available at Bioinformatics online.

Measuring and crust-correcting finite-frequency travel time residuals – application to southwestern Scandinavia

Abstract. We present a data-processing routine to compute relative finite-frequency travel time residuals using a combination of the Iterative Cross-Correlation and Stack (ICCS) algorithm and the Multi-Channel Cross-Correlation method (MCCC). The routine has been tailored for robust measurement of P- and S-wave travel times in several frequency bands and for avoiding cycle-skipping problems at the shortest periods. We also investigate the adequacy of ray theory to calculate crustal corrections for finite-frequency regional tomography in normal continental settings with non-thinned crust. We find that ray theory is valid for both P and S waves at all relevant frequencies as long as the crust does not contain low-velocity layers associated with sediments at the surface. Reverberations in the sediments perturb the arrival times of the S waves and the long-period P waves significantly, and need to be accounted for in crustal corrections. The data-processing routine and crustal corrections are illustrated using data from a~network in southwestern Scandinavia.

Iterative Cross-Correlation Analysis of Resting State Functional Magnetic Resonance Imaging Data

Seed-based cross-correlation analysis (sCCA) and independent component analysis have been widely employed to extract functional networks from the resting state functional magnetic resonance imaging data. However, the results of sCCA, in terms of both connectivity strength and network topology, can be sensitive to seed selection variations. ICA avoids the potential problems due to seed selection, but choosing which component(s) to represent the network of interest could be subjective and problematic. In this study, we proposed a seed-based iterative cross-correlation analysis (siCCA) method for resting state brain network analysis. The method was applied to extract default mode network (DMN) and stable task control network (STCN) in two independent datasets acquired from normal adults. Compared with the networks obtained by traditional sCCA and ICA, the resting state networks produced by siCCA were found to be highly stable and independent on seed selection. siCCA was used to analyze DMN in first-episode major depressive disorder (MDD) patients. It was found that, in the MDD patients, the volume of DMN negatively correlated with the patients' social disability screening schedule scores.

Combined iterative cross-correlation demodulation scheme for mixing space borne automatic identification system signals

Aiming at the potential presence of mixing automatic identification system (AIS) signals, a new demodulation scheme was proposed for separating other interfering signals in satellite systems. The combined iterative cross-correlation demodulation scheme, referred to as CICCD, yielded a set of single short signals based on the prior information of AIS, after the frequency, code rate and modulation index were estimated. It demodulates the corresponding short codes according to the maximum peak of cross-correlation, which is simple and easy to implement. Numerical simulations show that the bit error rate of proposed algorithm improves by about 40% compared with existing ones, and about 3 dB beyond the standard AIS receiver. In addition, the proposed demodulation scheme shows the satisfying performance and engineering value in mixing AIS environment and can also perform well in low signal-to-noise conditions.

Improving the correction of eddy current-induced distortion in diffusion-weighted images by excluding signals from the cerebral spinal fluid

Iterative cross-correlation (ICC) is the most popularly used schema for correcting eddy current (EC)-induced distortion in diffusion-weighted imaging data, however, it cannot process data acquired at high b-values. We analyzed the error sources and affecting factors in parameter estimation, and propose an efficient algorithm by expanding the ICC framework with a number of techniques: (1) pattern recognition for excluding brain ventricles; (2) ICC with the extracted ventricle for parameter initialization; (3) gradient-based entropy correlation coefficient (GECC) for optimal and finer registration. Experiments demonstrated that our method is robust with high accuracy and error tolerance, and outperforms other ICC-family algorithms and popular approaches currently in use.

Accuracy assessment of elastic strain measurement by EBSD

A detailed accuracy analysis of electron backscatter diffraction (EBSD) elastic strain measurement has been carried out using both simulated and experimental patterns. Strains are determined by measuring shifts between two EBSD patterns (one being the reference) over regions of interest (ROI) using an iterative cross-correlation algorithm. An original minimization procedure over 20 regions of interests gives a unique solution for the eight independent components of the deviatoric displacement gradient tensor. It is shown that this method leads to strain measurements on simulated patterns with an accuracy better than 10(-4). The influence of the projection parameters is also investigated. The accuracy assessment is illustrated by two worked examples: (i) four-point bending of a silicon single crystal and (ii) Si(1-x)Ge(x) layers on a Si substrate. Experimental results are compared with finite-element simulations.

A quantitative comparison of two methods to correct eddy current-induced distortions in DT-MRI

Eddy current-induced geometric distortions of single-shot, diffusion-weighted, echo-planar (DW-EP) images are a major confounding factor to the accurate determination of water diffusion parameters in diffusion tensor MRI (DT-MRI). Previously, it has been suggested that these geometric distortions can be removed from brain DW-EP images using affine transformations determined from phantom calibration experiments using iterative cross-correlation (ICC). Since this approach was first described, a number of image-based registration methods have become available that can also correct eddy current-induced distortions in DW-EP images. However, as yet no study has investigated whether separate eddy current calibration or image-based registration provides the most accurate way of removing these artefacts from DT-MRI data. Here we compare how ICC phantom calibration and affine FLIRT (http://www.fmrib.ox.ac.uk), a popular image-based multi-modal registration method that can correct both eddy current-induced distortions and bulk subject motion, perform when registering DW-EP images acquired with different slice thicknesses (2.8 and 5 mm) and b-values (1000 and 3000 s/mm(2)). With the use of consistency testing, it was found that ICC was a more robust algorithm for correcting eddy current-induced distortions than affine FLIRT, especially at high b-value and small slice thickness. In addition, principal component analysis demonstrated that the combination of ICC phantom calibration (to remove eddy current-induced distortions) with rigid body FLIRT (to remove bulk subject motion) provided a more accurate registration of DT-MRI data than that achieved by affine FLIRT.

On the use of the FLAIR technique to improve the correction of eddy current induced artefacts in MR diffusion tensor imaging

Eddy current induced geometric distortions can only be accurately corrected in brain diffusion-weighted echo-planar (DW-EP) images for b-values less than approximately 300 s mm−2 using the iterative cross-correlation (ICC) algorithm. This is due to the difference in signal intensity of the cerebrospinal fluid (CSF) compartment in the diffusion-weighted and baseline T2-weighted echo-planar (T2W-EP) images. At larger values of b, image misalignment artefacts can, however, be removed by directly correlating CSF-suppressed T2W-EP images with non-CSF-suppressed and CSF-suppressed DW-EP images. Separate phantom experiments can also be performed to provide eddy current calibration data. Here the ability of these methods to remove eddy current induced artefacts from DW-EP images collected in volunteer diffusion tensor imaging (DTI) experiments is investigated. Monte Carlo simulations show that in order for the ICC algorithm to produce accurate estimates of the eddy current induced distortions at b-values greater than 1000 s mm−2, the degree of CSF suppression should be greater than approximately 80%. This condition is typically met for FLAIR inversion times between 0.5 and 0.8 of the spin-lattice relaxation time of CSF. In volunteer studies the most complete image realignment was provided by direct correlation of CSF-suppressed T2W-EP and DW-EP images acquired in the FLAIR DTI experiment. These results indicate that although calibration data obtained from brain or phantom images can significantly reduce eddy current induced distortions, the optimum image realignment achievable using post-processing methods is likely to be that obtained by direct image warping techniques.

Blood Flow Velocity Measurement in Microcirculation Field by Means of Highly Accurate Iterative PIV.

Particle image velocimetry (PIV) has been the quantitative measurement method of instantaneous velocity fields in experimental fluid mechanics. The authors have proposed the adaptive PIV technique using the gradient-based sub-pixel analysis, in which a pixel unit displacement is obtained using the iterative cross-correlation method while a sub-pixel displacement is calculated by the use of the so-called spatio-temporal derivative method. The present method can achieve high accuracy of measurement and high spatial resolution compatibly. In this paper, the proposed method is applied to visualized images of the arteriole in the rat mesentery using the intravital-microscope and the high-speed digital video system. Time-series of the volocity profiles of the arteriole is obtained. Averaged velocity profiles show the blood flow volume is constant at five vessel cross-sections. The arteriole velocity profiles are blunt at the center region of the vessel cross-section and sharp profiles at near wall region, this suggests the shear stress on the vessel wall is higher than expected one. The results show that the proposed method is very useful to measure the blood flow velocity profiles with highly accuracy of measurement, and high temporal and spatial resolution.

A111高解像度PIV手法を用いた微小血管内の速度分布計測

Particle image velocimetry (PIV) is a quantitative method of measuring velocity fields instantaneously in experimental fluid mechanics. In order to improve the dynamic range, resolution and accuracy, the authors has proposed a new high-resolution PIV technique based on iterative cross-correlation and gradient method. The proposed method has the error of the order of 0.01 pixels.In this paper, the proposed method is applied to visualized images of the arteriole in the rat mesentery using the intravital-microscope and the high-speed digital video system. Time-series of the velocity profiles of the arteriole was obtained. Averaged velocity profiles shows the blood flow volume were constant at five vessel cross-sections. The arteriole velocity profile was blunt at center region of the vessel cross-section and sharp profiles at near wall region, this suggests the shear stress on the vessel wall is higher than expected. The results show that the proposed method is very useful to measure the blood flow velocity profiles with high accurate, temporal and spatial resolution.

Correction of eddy current-induced artefacts in diffusion tensor imaging using iterative cross-correlation

Geometric distortions of echo-planar images produced by the strong eddy currents present in the diffusion tensor imaging experiment are a major confound to the accurate quantification of diffusion coefficients, and measures of diffusion anisotropy based upon them. Here we investigate how the method of iterative cross-correlation (ICC) of baseline and diffusion-weighted images (DWIs) originally proposed by Haselgrove and Moore (Magn. Reson. Med. 36:960–964; 1996) can be extended to correct high b-value DWIs, without the need for extrapolation of distortion parameters determined from low b-value images. Monte Carlo simulations of synthetic brain images show that the maximum value of the trace of the b-matrix, Tr(b), at which distorted DWIs can be accurately corrected by direct comparison with the undistorted baseline image is approximately 300 s mm −2. Removal of the cerebrospinal fluid signal greatly extends this value of Tr(b) (up to approximately 2000 s mm −2), thereby allowing direct comparison of baseline and distorted images. The use of ICC distortion parameters determined from separate calibrations of water phantom images is also investigated, and found to be effective in correcting geometric distortions observed in the DWIs collected as part of a human volunteer diffusion tensor imaging study. This work suggests that distorted DWIs acquired at high values of b may be corrected using the ICC algorithm without collecting additional low b-value images, thus allowing simplified methods of measuring the apparent diffusion tensor D, based on collecting a small number of DWIs, to be implemented in quantitative patient examinations.